Harbor integration: serve Harbor task datasets through OpenEnv as trainable environments

.gitignore

@@ -141,3 +141,6 @@ docs/source/_env_assets/
 # Sphinx-gallery generated output
 docs/source/auto_getting_started/
 docs/source/sg_execution_times.rst
+
+# Gradio UI build artifacts
+.gradio/

docs/source/_toctree.yml

@@ -73,6 +73,8 @@
     title: Terminus
   - local: environments/coding_tools
     title: Coding Tools
+  - local: environments/harbor
+    title: Harbor
   - local: environments/chat
     title: Chat
   - local: environments/atari

envs/harbor_env/__init__.py

@@ -0,0 +1,33 @@
+"""harbor_env: run Harbor tasks with token-level capture.
+
+The implementation lives in `openenv.harbor` and `openenv.core.harness.capture`; this package is
+deployment packaging only (manifest, Dockerfile, ASGI entry point). The client and result models are
+re-exported here so `from harbor_env import HarborEnv` works, matching every other environment.
+
+Examples:
+
+```python
+from harbor_env import HarborEnv
+
+with HarborEnv(base_url="http://localhost:8000") as env:
+    split = env.splits()[0]["name"]
+    result = env.run_rollout(split=split, task_index=0, harness="opencode", sandbox="e2b")
+    print(result.reward, result.n_turns)
+```
+"""
+
+from openenv.harbor.client import HarborEnv
+from openenv.harbor.models import (
+    HarborConversation,
+    HarborRolloutResult,
+    HarborTaskRef,
+    HarborTurn,
+)
+
+__all__ = [
+    "HarborEnv",
+    "HarborConversation",
+    "HarborRolloutResult",
+    "HarborTaskRef",
+    "HarborTurn",
+]

envs/harbor_env/client.py

@@ -0,0 +1,5 @@
+"""Typed client for a deployed harbor_env."""
+
+from openenv.harbor.client import HarborEnv
+
+__all__ = ["HarborEnv"]